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Nathan L. Brouwer Phd
Computational ecologist specializing in complex datasets
I am a computational ecologist, data scientist and educator with 15 years of experience extracting insights from messy data and communicating the results. I specialize in using generalized-linear mixed models (GLMMs) to analyze data from long-term observational studies. Since my PhD I have also taught myself key aspects of bioinformatics, phylogenetics, machine learning and population genomics.
Education
Seattle Pacific University
B.S. in Biology, with Honors
N/A
2002
University of Pittsburgh
Phd in Biological Sciences - Ecology & Evolution
N/A
2015
Research & Professional Experience
Associate Teaching Professor - General & Quantitative Biology
Unv. of Pittsburgh Dept. of Biological Sciences
N/A
2019 - present
- R Programming Develop & deliver curriculum for updated Computational Biology course (4 semesters)
- Data analysis Instructor for biostatistics (1 semester) & consult on statistics curriculum development for lab classes.
- Science communication: Teach scientific writing, non-majors biology, intro biology lecture & labs.
Post-doctoral Research Associate - Avian Conservation
National Aviary of Pittsburgh
N/A
2015 - 2019
- GLMMS: Analyze decade-long tropical bird population & community datasets.
- Data Cleaning: Clean & merge diverse datasets of environmental data & organism traits.
- R packages: Implement models on migratory birds as reproducible software,
- Computational Statistics: Develop sensitivity and uncertainty analyses methods for for migration models.
Adjunct Professor - Biology
La Roche College, Pittsburgh
N/A
2018-2019 (Fall & Winter)
- Co-taught intro to research course (fall) & scientific writing (spring)
- Developed new lab and data analysis activities
Adjunct Professor - Biological Data Analysis
Dusquesne Unv. & California Unv. of PA
N/A
2016-2017
- R programming & data analysis: Teach graduate (Dusquesne, Spring 2017) & undergraduate data analysises courses (CalU, Fall 2016 & 2017)
PASSIONS
BAYESIAN STATISTICS: Bayesian approaches have always been apparent to me as the optimal way to approach complex ecological data. Having recently made the time to start using rstan, I’m excited to explore the possibilities of working directly in Stan.
MACHINE LEARNING: While working through Kaggle exercises this summer to prepare for my most recent Computational Biology class, the beauty and power of supervised ML methods were revealed to me.
REPRODUCIBLE WORKFLOWS …
Graduate Research Assistant
Department of Biological Sciences, University of Pittsburgh
N/A
2010 - 2015
- Data Cleaning: Update, clean and manage decade-long plant demographic experiment.
- GLMMs: Determine appropriate model structures and analyze data.
- Field work: Design & carry out research on plant demography.
Peace Corps Volunteer - Agroforestry Outreach
National Agricultural Research Institute, The Gambia, West Africa
N/A
2004 - 2006
- Assist in staff development, including data analysis & experimental design,
- Conducted outreach and training on agroforestry & sustainable agriculture
Infecious Disease Research Scientist
University of Washington Department of Allergy & Infectious Disease
N/A
2002 - 2004
- Lab work: Conduct experiments on pathogen cell-adhesion proteins.
Publications - Science Education
Computational Biology for All! An open access book for bioinformatics & computational biology vs 0.9
Open-access computational biology textbook.
N/A
2022
Brouwer
A Little Book of R for Bioinformatics vs. 2.0
Open-access bioinformatics primer.
N/A
2022
Coghlan (au.) & Brouwer (ed., au)
Foundations of Biology and Environmental Science: An Open-Access Encyclopedia
Compilation of Open-Access resources on general biology, computational bioloyg, and environmental science.
N/A
N/A
Brouwer (ed., au.)
R Packages
Population Modeling: redstart: An R package for Periodic Full-Annual Cycle Avian Population Models and Monte-Carlo simulation.
R implementation & replication of Runge & Marra (2005) Modeling Seasonal Interactions in the Population Dynamics of Migratory Birds.
N/A
N/A
Brouwer et al.
- Website & Tutorials: brouwern.github.io/FACavian/index.html
Publications
Population Models: Direct effects of a non-native invader erode native plant fitness in the forest understory
Journal of Ecology 108:189–198
N/A
2019
Bialic-Murphy, Brouwer & Kaliz.
Data & Code: Dryad
NMDS: Stream acidification & reduced availability of pollution-sensitive aquatic insects are associated with dietary shifts in a stream-dependent Neotropical migratory songbird.
PeerJ 6:e5141
N/A
2019
Trevelline, Nuttle, Porter, Brouwer et al.
NMDS: DNA metabarcoding reveals the importance of aquatic prey subsidies & the structure of dietary niches in a community of breeding riparian songbirds.
Oecologia 187: 85-98
N/A
2018
Trevelline, Brouwer et al.
GLMM: Avian community characteristics & demographics reveal how conservation value of regenerating tropical dry forests changes with forest age
PeerJ 6: e5217
N/A
2018
Latta, Brouwer et al.
Data & Code: GitHub
GLMM: Long-term monitoring reveals an avian species credit in secondary forest patches of Costa Rica
PeerJ 6: e3539
N/A
2017
Latta, Brouwer et al.
Data & Code: Harvard Dataverse
GAMM: Increased photosynthetic performance of an invasive forest herb mediated by deer overabundance.
AoB Plants 9: plx011
N/A
2017
Heberling, Brouwer & Kalisz.
GAMM Code: GitHub
Data & Code:AoB Plants
GLMM: Mutualism-disrupting allelopathic invader drives carbon stress & vital rate decline in a forest perennial herb.
AoB Plants 7: plv014
N/A
2015
Brouwer, Hale & Kalisz.